JP2023147286A - Program, information processing device, information processing system, and information processing method - Google Patents

Abstract

To allow for attracting sufficient attention of other users on images and videos when posting them on an SNS service, etc.SOLUTION: A program provided herein makes a computer perform an acquisition step of acquiring image data, and a generation step of generating generation data based on the image data acquired in the acquisition step, where the generation step uses the image data as an input image to generate generation data that increases a response index .SELECTED DRAWING: Figure 10

Description

The present disclosure relates to a program, an information processing device, an information processing system, and an information processing method.

2. Description of the Related Art There is a known technology for automatically capturing still images, videos, etc. using a life log camera.
Patent Document 1 discloses an information processing device that can protect privacy by converting a person's facial image so that it does not look strange.
Patent Document 2 discloses a technique for suppressing limitations on the shooting time for video shooting spontaneously performed by a user in a life log camera.
Patent Document 3 discloses an imaging device that allows the user to acquire a video of his or her preference without the user performing any special operations.

JP2014-85796 JP2021-145556 JP2020-205637

When posting articles containing images, videos, text, etc. taken with a camera to SNS services (Social Networking Services), etc., there is a problem in which it is not possible to sufficiently arouse other users' interest in the posted article. There is.

Therefore, the present disclosure has been made to solve the above-mentioned problems, and its purpose is to provide a technology for generating image data that can be expected to have a larger response in SNS services.

A program to be executed by a computer including a processor and a storage unit, the program includes an acquisition step of acquiring digital content, and a generation step of generating generated data based on the digital content acquired in the acquisition step. and a generation step is a step of generating generated data such that a response index becomes larger by using digital content as input data.

According to the present disclosure, it is possible to generate image data that can be expected to have a greater response in SNS services.

1 is a diagram showing the overall configuration of an information processing system 1. FIG. 1 is a block diagram showing the functional configuration of a server 10. FIG. 2 is a block diagram showing the functional configuration of a user terminal 20. FIG. 3 is a block diagram showing the functional configuration of a photographing terminal 30. FIG. 3 is a diagram showing a data structure of a user table 1012. FIG. 3 is a diagram showing a data structure of a photographing table 1013. FIG. 3 is a diagram showing the data structure of an image table 1014. FIG. 3 is a diagram showing a data structure of an SNS table 1015. FIG. 3 is a diagram showing a data structure of an image processing table 1016. FIG. It is a flowchart showing the operation of SNS posting processing. 2 is a block diagram showing the basic hardware configuration of a computer 90. FIG.

Embodiments of the present disclosure will be described below with reference to the drawings. In all the figures explaining the embodiments, common components are given the same reference numerals and repeated explanations will be omitted. Note that the following embodiments do not unduly limit the content of the present disclosure described in the claims. Furthermore, not all components shown in the embodiments are essential components of the present disclosure. Furthermore, each figure is a schematic diagram and is not necessarily strictly illustrated.

<Overview of information processing system 1>
FIG. 1 is a diagram showing the overall configuration of an information processing system 1. As shown in FIG. An information processing system 1 in the present disclosure is an information processing system that posts digital content including image data captured by a camera, video data, audio data collected by a microphone, etc. to an SNS service. Digital content is content information that includes any data such as image data, video data, audio data, music data, etc. that is captured by a camera or collected by a microphone.

<Basic configuration of information processing system 1>
FIG. 1 shows an information processing system 1 in the present disclosure. The information processing system 1 includes a server 10, a plurality of user terminals 20A, 20B, and 20C, and a plurality of photographing terminals 30A, 30B, and 30C, which are connected via a network N. FIG. 2 is a block diagram showing the functional configuration of the server 10. FIG. 3 is a block diagram showing the functional configuration of the user terminal 20. FIG. 4 is a block diagram showing the functional configuration of the photographing terminal 30.

The server 10 is an information processing device that provides a service that acquires image data photographed by the photographing terminal 30, generates image data that can be expected to receive a greater response in an SNS service, and posts the image data to the SNS service.

The user terminal 20 is an information processing device operated by a user who uses the service. The photographing terminal 30 is an information processing device for controlling cameras that constitute the service. The user terminal 20 and the photographing terminal 30 may be, for example, a mobile terminal such as a smartphone or a tablet, a stationary PC (Personal Computer), or a laptop PC. Further, it may be a wearable terminal such as an HMD (Head Mount Display) or a wristwatch type terminal.
Note that the photographing terminal 30 is preferably installed at a predetermined fixed location, and the camera 3061 is preferably a fixed-point camera provided to photograph a predetermined range.

Each information processing device is composed of a computer including an arithmetic unit and a storage device. The basic hardware configuration of the computer and the basic functional configuration of the computer realized by the hardware configuration will be described later. Regarding each of the server 10, user terminal 20, and photographing terminal 30, explanations that overlap with the basic hardware configuration and basic functional configuration of the computer, which will be described later, will be omitted.

The configuration and operation of each device will be explained below.

<Functional configuration of server 10>
FIG. 2 shows a functional configuration realized by the hardware configuration of the server 10. The server 10 includes a storage section 101 and a control section 104.

<Configuration of storage unit of server 10>
The storage unit 101 of the server 10 includes an application program 1011, a user table 1012, a shooting table 1013, an image table 1014, an SNS table 1015, an image processing table 1016, an evaluation model 1017, a generation model 1018, a region extraction model 1019, and an image processing model 1020. , an event model 1021, and a text model 1022.
FIG. 5 is a diagram showing the data structure of the user table 1012. FIG. 6 is a diagram showing the data structure of the imaging table 1013. FIG. 7 is a diagram showing the data structure of the image table 1014. FIG. 8 is a diagram showing the data structure of the SNS table 1015. FIG. 9 is a diagram showing the data structure of the image processing table 1016.

The user table 1012 is a table that stores and manages information about member users (hereinafter referred to as users) who use the service. When a user registers to use a service, the user's information is stored in a new record in the user table 1012. This allows the user to use the service according to the present disclosure.
The user table 1012 is a table that uses the user ID as a primary key and has columns of user ID and user name.

The user ID is an item that stores an identifier for uniquely identifying a user.
The user name is an item that stores the user's name. The user name may be any character string such as a nickname instead of a full name.

The photographing table 1013 is a table for storing moving image data and audio data (photographing data) photographed by a camera. The photographing table 1013 is a table having columns of user ID, video data, and audio data, with camera ID as the main key.

The camera ID is an item that stores photographic device identification information for identifying the camera. The photographic device identification information is an item in which a unique value is set for each camera.
The user ID is an item that stores user identification information of a user associated with the photographing terminal 30.
The video data is an item that stores video data captured by a camera. Reference information (path) to a video data file located at another location may also be stored. The format of the video data may be any data format such as MP4, MOV, WMV, AVI, AVCHD.
Audio data is an item that stores audio data collected by a microphone. Reference information (paths) for audio data files located elsewhere may also be stored. The format of the audio data may be any data format such as AAC, ATRAC, mp3, mp4, etc.

The image table 1014 is a table for storing image data periodically photographed by a camera.
The image table 1014 is a table having columns of camera ID, image data, event attribute, generated data, text, hashtag, posting date and time, response index, and photographing date and time.

The camera ID is an item that stores the photographing device identification information of the camera that photographed the image.
Image data is an item that stores image data taken by a camera. Reference information (paths) for image data files located elsewhere may also be stored. The format of the image data may be any data format such as jpeg, png, bmp, tiff, gif, eps, and svg.
The event attribute is an item that stores information regarding the event attribute of image data (event attribute information) inferred based on image data captured by a camera. The event attribute information is arbitrary object data that includes information such as event names and event classifications such as birthdays, welcome parties, farewell parties, year-end parties, recitals, and celebration parties.
The generated data is an item that stores generated data generated based on image data photographed by a camera.
Text is an item that stores text information inferred based on image data captured by a camera.
A hash tag is an item that stores text information used as a hash tag inferred based on image data captured by a camera. A hashtag is text with a hash symbol "#" added in front of it. In an SNS service, a posted article can be tagged by including a hashtag in the posted article.
The posting date and time is an item that stores the date and time at which a posted article consisting of generated data, text, and a hashtag is posted.
The reverberation index is an item that stores an index inferred based on generated data and the like. The response index is calculated based on the posting date and time when a user associated with image data posts an article consisting of generated data, text, and hashtags to an SNS service, and other users respond to the posted article. It is an index of the degree of response (number of likes, number of retweets, number of comments) that can be expected to be received from a post. The larger the response index, the greater the response from other users to the posted article can be expected.
An example of a response index is the sum of likes, retweets, and comments.
The shooting date and time is an item that stores the shooting date and time of image data.

The SNS table 1015 is a table that stores attribute information (SNS attributes) regarding the user's SNS service.
The SNS table 1015 is a table having columns of SNS ID, end point, user ID, service name, number of followers, follower attribute, user attribute, and upper limit on the number of posts.
The SNS attribute is information regarding user attributes in the SNS service of a user who uses the SNS service. Specifically, the SNS attribute is information including information such as the number of followers, follower attributes, user attributes, etc. of the user who uses the SNS service.

The SNSID is an item that stores identification information of the user's account in the SNS service. A user can use various services provided by the SNS service using the SNS ID.
The endpoint is an item that stores a URL (Uniform Resource Locator) when posting an article to an SNS service. By sending a predetermined request to the endpoint, various processing can be requested to the SNS service via an API (Application Programming Interface).
For the endpoint, different URLs are stored for each SNS service and for each type of processing provided by the SNS service.
The user ID is an item that stores user identification information of the user associated with the SNSID.
The service name is an item that stores the name of the SNS service. There are various SNS service names such as Twitter, Facebook, Instagram, and LinkedIn.
The number of followers is the number of other users who follow the user's account on the SNS service. Following refers to the state of being linked to a specific account in an SNS service for the purpose of following the activities of that specific account.
The follower attribute is an item that stores information regarding the attributes of followers (follower attribute information). Follower attribute information is arbitrary object data that includes information regarding attributes of followers, such as the follower's gender, age, occupation, and interests (cooking, travel, cars, computers, games, etc.).
Follower attribute information may be obtained from the operator of the SNS service, or may be specified based on the content of articles posted by followers.
User is an item that stores information regarding the attributes of the user of the user's account in the SNS service (user attribute information). The user attribute information is arbitrary object data that includes information regarding user attributes such as the user's gender, age, occupation, and interests (cooking, travel, automobiles, computers, games, etc.).
The user attribute information may be input by the user himself or may be specified based on the content of the user's posted article.
The upper limit number of posts is an item that stores the upper limit number of posts within a predetermined period when a user posts an article to an SNS service. As the predetermined period, it is possible to set an upper limit value for the number of postings in any period such as one day, one week, one month, or every day of the week.

The image processing table 1016 is a table that stores information (image processing information) related to image processing such as effects and filters applied to image data.
The image processing table 1016 is a table having columns for image processing ID and image processing content.

The image processing ID is an item that stores image processing identification information for identifying the content of image processing.
The image processing content is an item that stores the image processing content to be applied to image data. References to functions, methods, programs, etc. that perform image processing located elsewhere may be stored.
The image processing content includes a frame for setting a frame for image data. Image processing contents include monotone, sepia, vivid, etc. for correcting the color tone of image data. The image processing contents include contrast correction, brightness correction, etc. for image data. The image processing includes face correction processing for correcting various parts of a person's face included in the image data, such as eyes, nose, and mouth. The image processing includes face synthesis processing that corrects the face of a person included in the image data into the face of an animal, character, or the like. The image processing content includes processing for synthesizing characters, accessories, decorations, etc. with image data.
The image processing includes processing for synthesizing image data with an effect or image that draws attention to a predetermined area such as a human object included in the image data. Specifically, when an image includes a dining scene at a restaurant, etc., it is possible to add decorations that attract attention to the table where customers are sitting, image processing to make the food look more delicious, or to create an atmosphere that makes the meeting look fun. This includes image processing such as

The evaluation model 1017 is a learning model that outputs (infers) a reverberation index using image data as input data. The learning process of the evaluation model 1017 will be described later. The input data may include SNS attributes such as the number of followers, follower attributes, user attributes, etc. in the user's SNS service related to the image data.
The input data may include the text and hashtag of the posted article related to the image data.
The output data may include information regarding posting date and time.

The generation model 1018 is a learning model that uses image data as input data and outputs (inferences) generated data. The learning process of the generative model 1018 will be described later.
The input data may include event attributes associated with the image data.

The region extraction model 1019 is a learning model that uses image data as input data and outputs (infers) region-extracted image data (partial image data). The learning process of the region extraction model 1019 will be described later.
The input data may include event attributes associated with the image data.

The image processing model 1020 is a learning model that uses partial image data as input data and outputs (infers) image data that has undergone image processing (image processing data). The learning process of the image processing model 1020 will be described later.
The input data may include event attributes associated with the image data.

The event model 1021 is a learning model that outputs (infers) event attributes using image data as input data. The learning process of the event model 1021 will be described later.

The text model 1022 is a learning model that outputs (infers) text or hashtags using image data as input data. The learning process for the text model 1022 will be described later.
The input data may include event attributes associated with the image data.

A deep learning model using a deep neural network in deep learning will be described as an example of an evaluation model 1017, a generation model 1018, a region extraction model 1019, an image processing model 1020, an event model 1021, and a text model 1022. The learning model does not necessarily have to be a deep learning model, and may be any machine learning model, artificial intelligence model, or the like.

<Configuration of control unit of server 10>
The control unit 104 of the server 10 includes a user registration control unit 1041, a posting unit 1042, an event inference unit 1043, an image generation unit 1044, a text generation unit 1045, and an evaluation unit 1046. The control unit 104 realizes each functional unit by executing the application program 1011 stored in the storage unit 101.

The user registration control unit 1041 performs a process of storing information of users who wish to use the service according to the present disclosure in the user table 1012 and the shooting table 1013.
The information on the user ID and user name stored in the user table 1012 is obtained when the user opens a web page operated by a service provider from any information processing terminal and enters the user ID and user name in a predetermined input form. Send to server 10. The user registration control unit 1041 of the server 10 stores the received user ID and user name in a new record of the user table 1012, and the user registration is completed. This allows the users stored in the user table 1012 to use the service.
The user ID information stored in the shooting table 1013 is such that the user opens a web page operated by a service provider from any information processing terminal, and enters the camera of the shooting terminal 30 that the user wants to associate with his or her account in a predetermined input form. Enter or select the ID and send it to the server 10. The user registration control unit 1041 of the server 10 stores the user ID of the user in the photographing table 1013 in association with the received camera ID. Thereby, the user stored in the user table 1012 and the photographing terminal 30 are associated. A user associated with a photographing terminal 30 in the photographing table 1013 can receive services according to the present disclosure using the photographing terminal 30 associated with the user.
Prior to the registration of user information in the user table 1012 by the user registration control unit 1041, the service provider may perform a predetermined examination to restrict whether or not the user can use the service.
The user ID may be any character string or number that can identify the user, and may be any character string or number desired by the user, or may be automatically set by the user registration control unit 1041 of the server 10. It's okay.

The posting unit executes SNS posting processing. Details will be described later.
The event inference unit executes event inference processing. Details will be described later.
The image generation unit executes image generation processing (first embodiment) and image generation processing (second embodiment). Details will be described later.
The text generation unit executes text hashtag generation processing. Details will be described later.

<Functional configuration of user terminal 20>
FIG. 3 shows a functional configuration realized by the hardware configuration of the user terminal 20. The user terminal 20 includes a storage unit 201, a control unit 204, a touch panel 206, a touch sensitive device 2061, a display 2062, a microphone 2081, a speaker 2082, a position information sensor 2083, a camera 2084, and a motion sensor 2085.
Note that instead of the server 10, the user terminal 20 may include a posting section 1042, an event inference section 1043, an image generation section 1044, a text generation section 1045, an evaluation section 1046, or a combination of at least two of these. At that time, the evaluation model 1017, the generation model 1018, the region extraction model 1019, the image processing model 1020, the event model 1021, and the text model 1022 may be downloaded from the server 10 to make each functional unit function.

<Configuration of storage unit of user terminal 20>
The storage unit 201 of the user terminal 20 stores a user ID 2011 for identifying a user who uses the user terminal 20 and an application program 2012.
The user ID is the user's account ID. The user transmits the user ID 2011 from the user terminal 20 to the server 10. The server 10 identifies the user based on the user ID 2011 and provides the service according to the present disclosure to the user. Note that the user ID includes information such as a session ID temporarily given by the server 10 to identify the user using the user terminal 20.
The application program 2012 may be stored in advance in the storage unit 201, or may be downloaded from a web server operated by a service provider via a communication IF. Application program 2012 includes an interpreted programming language such as JavaScript® that is executed on a web browser application stored on user terminal 20.

<Configuration of control unit of user terminal 20>
The control unit 204 of the user terminal 20 includes an input control unit 2041 and an output control unit 2042. The control unit 204 implements functional units of an input control unit 2041 and an output control unit 2042 by executing the application program 2012 stored in the storage unit 201.
The input control unit 2041 of the user terminal 20 controls the content of operations performed by the user on the touch-sensitive device 2061 on the touch panel 206, voice input to the microphone 2081, output from input devices such as the position information sensor 2083, the camera 2084, and the motion sensor 2085. Acquires information and executes various processes. The input control unit 2041 of the user terminal 20 executes a process of transmitting information acquired from the input device to the server 10 together with the user ID 2011.
The output control unit 2042 of the user terminal 20 receives user operations on the input device and information from the server 10, and executes control processing of the display content of the display 2062 and the audio output content of the speaker 2082.

<Functional configuration of photographing terminal 30>
FIG. 4 shows a functional configuration realized by the hardware configuration of the photographing terminal 30. The photographing terminal 30 includes a storage section 301 and a control section 304. The photographing terminal 30 is connected to an input device 306. Input device 306 includes a camera 3061 and a microphone 3062.
The camera 3061 is a camera that is installed at a predetermined location in a store or residence, and takes pictures of people, customers, etc. located at predetermined locations in the store or residence. Specifically, the camera 3061 is placed within a facility such as a restaurant, facing a table where a customer is seated. Alternatively, it can be installed in the center of a table where customers are sitting, and can take pictures of the customers sitting at the table.

<Configuration of storage unit of photographing terminal 30>
The storage unit 301 of the photographing terminal 30 stores a camera ID 3011 for identifying the camera 3061 connected to the photographing terminal 30 and an application program 3012.
The user transmits the camera ID 3011 from the photographing terminal 30 to the server 10.
The application program 3012 may be stored in advance in the storage unit 301, or may be downloaded from a web server operated by a service provider via a communication IF. The application program 3012 includes an interpreted programming language such as JavaScript (registered trademark) that is executed on a web browser application stored in the photographing terminal 30.

<Configuration of control unit of photographing terminal 30>
The control section 304 of the photographing terminal 30 includes an input control section 3041 and an output control section 3042. The control unit 304 implements functional units of an input control unit 3041 and an output control unit 3042 by executing the application program 3012 stored in the storage unit 301.
The input control unit 3041 of the photographing terminal 30 acquires image data photographed by the camera 3061, video data, and audio data collected by the microphone 3062, and executes various processes. The input control unit 3041 of the photographing terminal 30 executes a process of transmitting image data, video data, and audio data together with the camera ID 3011 to the server 10.

<Operation of information processing system 1>
Each process of the information processing system 1 will be explained below.
FIG. 10 is a flowchart showing the operation of the SNS posting process in the server 10.

<SNS posting process>
The SNS posting process is a process of generating posted article information to be posted to the SNS service based on digital content such as image data taken by the photographing terminal 30, and executing the posting.

<Overview of SNS posting process>
The SNS posting process is a series of processes that acquires image data taken by the shooting terminal 30 and executes event inference processing, image generation processing, text/hashtag generation processing, evaluation processing, SNS posting judgment, and SNS posting processing. be. The SNS posting process is periodically executed at predetermined intervals. Note that in the SNS posting process, a series of processes may be applied to arbitrary digital content instead of image data.

<Details of SNS posting process>
In step S101, the input control unit 3041 of the photographing terminal 30 controls the camera 3061 to perform photographing control, thereby photographing and acquiring image data and video data.
In response to an input operation by the user, the input control unit 3041 of the photographing terminal 30 may start the SNS posting process. For example, the SNS posting process may be started by the user operating a predetermined switch provided on the photographing terminal 30 or the camera 3061. The user terminal 20 receives the input operation by the user, and in response to the input operation, transmits a request to the input control unit 3041 of the photographing terminal 30 to instruct to start SNS posting processing, and controls the input of the photographing terminal 30. The unit 3041 may start SNS posting processing in response to receiving the request.
The input control unit 3041 of the photographing terminal 30 collects and acquires audio data by controlling the microphone 3062 to perform sound collection control. The photographing terminal 30 transmits the camera ID 3011, photographed image data, moving image data, and collected audio data to the server 10.
The posting unit 1042 of the server 10 stores the received image data in the image data item of the image table 1014 in association with the received camera ID. The posting unit 1042 of the server 10 stores the shooting date and time of the image data, the reception date and time of the image data, the current date and time, etc. in the current date and time item of the image table 1014. The posting unit 1042 of the server 10 stores the image data acquired from the photographing terminal 30 in a new record of the image table 1014 every time the SNS posting process is executed periodically.
The posting unit 1042 of the server 10 stores the video data and audio data in the video data and audio data items of the photographing table 1013 in association with the received camera ID. The posting unit 1042 of the server 10 may be configured to always store the video data and audio data acquired from the imaging terminal 30 in the video data and audio data items of the imaging table 1013. The posting unit 1042 of the server 10 edits the video data and audio data acquired from the shooting terminal 30 into video data and audio data of a certain length, respectively, and stores them in the video data and audio data items of the shooting table 1013. It is also possible to have a configuration in which

<Event inference processing>
In step S102, the event inference unit 1043 of the server 10 executes event inference processing. The event inference process is a process of inferring event attributes based on image data photographed by the photographing terminal 30.

The event inference unit 1043 of the server 10 uses the image data newly stored in the image table 1014 as input data and applies it to the event model 1021 to obtain event attributes as output data.
The event inference unit 1043 of the server 10 stores the output event attribute in the event attribute field of the corresponding record in the image table 1014.

<Image generation processing>
In step S103, the image generation unit 1044 of the server 10 executes image generation processing. The image generation process is either image generation process (first embodiment) or image generation process (second embodiment).

<Image generation processing (first embodiment)>
The image generation process (first embodiment) is a process of generating generated data based on image data photographed by the photographing terminal 30.

<Overview of image generation processing (first embodiment)>
The image generation process (first embodiment) is a series of processes that generates generated data by applying image data photographed by the photographing terminal 30 to the generation model 1018 as input data.

<Details of image generation processing (first embodiment)>
The image generation unit 1044 of the server 10 uses the image data and event attributes newly stored in the image table 1014 as input data, and applies them to the generation model 1018 to obtain generated data as output data. Note that the event attribute does not need to be included in the input data.
The image generation unit 1044 of the server 10 stores the output generated data in the generated data item of the corresponding record in the image table 1014.

<Image generation processing (second embodiment)>
The image generation process (second embodiment) is a process of generating generated data based on image data photographed by the photographing terminal 30.

<Overview of image generation processing (second embodiment)>
The image generation process (first embodiment) uses the image data photographed by the photographing terminal 30 as input data, generates partial image data by applying the area extraction model 1019, uses the partial image data as input image data, This is a series of processing that generates image processing data by applying the image processing model 1020.

<Details of image generation processing (second embodiment)>
The image generation unit 1044 of the server 10 applies the image data and event attributes newly stored in the image table 1014 as input data to the region extraction model 1019, thereby acquiring partial image data as output data. Note that the event attribute does not need to be included in the input data.
The image generation unit 1044 of the server 10 uses the partial image data and event attributes as input data and applies them to the image processing model 1020 to obtain image processing data as output data. Note that the event attribute does not need to be included in the input data.
The image generation unit 1044 of the server 10 stores the output image processing data in the generated data item of the corresponding record in the image table 1014.

<Text/hashtag generation process>
In step S104, the text generation unit 1045 of the server 10 executes text hashtag generation processing.
The text generation unit 1045 of the server 10 uses the image data and event attributes newly stored in the image table 1014 as input data and applies them to the text model 1022 to obtain text and hashtags as output data. Note that the event attribute does not need to be included in the input data.
The text generation unit 1045 of the server 10 stores the output text and hash tag in the text and hash tag field of the corresponding record in the image table 1014.

<Evaluation processing>
In step S105, the evaluation unit 1046 of the server 10 executes evaluation processing. The evaluation process is a process for inferring the response index and posting date and time of the image data using generated data or image processing data as input data.

<Overview of evaluation process>
The evaluation process involves acquiring generated data or image processing data, acquiring text and hashtags, acquiring SNS attributes of the user associated with the photographing terminal 30, and applying them to the evaluation model 1017 as input data. This is a series of processes to infer the response index and posting date and time.

<Details of evaluation processing>
The evaluation unit 1046 of the server 10 acquires the generated data, text, and hashtag newly stored in the image table 1014. The evaluation unit 1046 of the server 10 searches the photographing table 1013 based on the camera ID of the corresponding record in the image table 1014, and identifies the user ID of the user associated with the photographing terminal 30. The evaluation unit 1046 of the server 10 searches the SNS table 1015 based on the specified user ID, and obtains SNS attributes including the number of followers, follower attributes, and user attributes.
The evaluation unit 1046 of the server 10 uses the generated data, text, hashtag, and SNS attribute as input data and applies them to the evaluation model 1017 to obtain the response index and posting date and time as output data. Note that text, hashtags, and SNS attributes do not need to be included in the input data.
The evaluation unit 1046 of the server 10 stores the response index and posting date and time in the response index and posting date and time fields of the corresponding record in the image table 1014. Note that the posting date and time does not need to be included in the output data.

In step S106, the posting unit 1042 of the server 10 acquires the response index newly stored in the image table 1014. The posting unit 1042 of the server 10 ends the process if the response index is less than a predetermined value (reaction threshold) (in the case of No). If the condition that the reverberation index is greater than or equal to the predetermined value is satisfied, the process proceeds to step S107.

The echo threshold may be a predetermined value manually determined by the service provider.
The response threshold is based on the value of the upper limit of the number of posts in the SNS table 1015 of the corresponding user, and articles with the highest response index as possible are posted to the SNS service within the range where the number of posted articles within a predetermined period falls within the upper limit of the number of posts. This is the value determined as follows. The echo threshold may be automatically determined using known machine learning, deep learning, artificial intelligence techniques, or the like.

In step S107, the posting unit 1042 of the server 10 acquires the generated data, text, hashtag, and posting date and time newly stored in the image table 1014. The posting unit 1042 of the server 10 generates predetermined object data (posted article) in JSON format or the like for posting to an SNS service based on the obtained generated data, text, and hashtag.
The posting unit 1042 of the server 10 searches the photographing table 1013 based on the camera ID of the corresponding record in the image table 1014, and identifies the user ID of the user associated with the photographing terminal 30. The posting unit 1042 of the server 10 searches the SNS table 1015 based on the specified user ID, and obtains the values of the SNS ID and endpoint.
The posting unit 1042 of the server 10 sends a request including the SNSID, which is the user ID of the user in the SNS service, and the posted article to the acquired endpoint so that the posted article is posted on the SNS service at the posting date and time. As a result, the article is posted to the SNS service at the posting date and time.

For example, if the photographed image includes a dining scene at a restaurant, etc., decorations that attract attention may be added to the table where customers are sitting, image processing to make the food taste more delicious, or functions such as gatherings may be added. Image processing is applied to create that atmosphere, and data that is expected to have a greater impact on SNS is generated and posted to SNS services.

<Learning process>
The learning process of the evaluation model 1017, generation model 1018, region extraction model 1019, image processing model 1020, event model 1021, and text model 1022 will be described below.

<Learning process of evaluation model 1017>
The learning process of the evaluation model 1017 is a process of learning the learning parameters of the deep neural network included in the evaluation model 1017 by deep learning.

<Overview of learning process of evaluation model 1017>
The learning process of the evaluation model 1017 uses image data, text, and hashtags included in past articles posted on SNS services as input data (input vectors), and uses response index and posting date and time of posted articles as output data (teacher data). This is a process of learning the learning parameters of the deep neural network included in the evaluation model 1017 by deep learning.
Either the text or the hashtag may be omitted from the input data of the evaluation model 1017.
The posting date and time may be omitted from the output data of the evaluation model 1017.

<Details of learning process of evaluation model 1017>
The learning unit 1051 of the server 10 acquires SNS attributes of various users in the SNS service, including the number of followers for each user, follower attributes, and user attributes, from a known SNS service. The learning unit 1051 of the server 10 acquires posted articles including image data, text, hashtags, etc., posted in the past by the user from the SNS service. The learning unit 1051 of the server 10 calculates the response index (teacher response index) for each past article posted based on the number of likes, number of retweets, and number of comments within a predetermined period after posting the article. The predetermined period may be any time or period. As an example of a calculation method, the sum of the number of likes, number of retweets, and number of comments is calculated.
The learning unit 1051 of the server 10 uses, for each posted article, the SNS attributes of the user who posted the posted article, image data, text, hashtags, etc. included in the posted article as input data (input vector), and calculates the teacher response index, the posted Learning data is created so that information about the posting date and time, such as the posting time or posting day of the article, becomes output data (teacher data).
The learning unit 1051 of the server 10 creates datasets such as training data, test data, and verification data for learning the deep neural network of the evaluation model 1017 based on the learning data.
The learning unit 1051 of the server 10 uses deep learning to learn the learning parameters of the deep neural network included in the evaluation model 1017 based on the created data set.

<Learning process of generative model 1018>
The learning process of the generative model 1018 is a process of learning the learning parameters of the deep neural network included in the generative model 1018 by deep learning.

<Overview of learning process of generative model 1018>
The learning process of the generative model 1018 uses image data as input data (input vector), and uses deep learning to adjust the learning parameters of the deep neural network included in the generative model 1018 so as to output generated data that can obtain a larger reverberation index. This is a learning process.

<Details of the learning process of the generative model 1018>
The learning unit 1051 of the server 10 acquires SNS attributes of various users in the SNS service, including the number of followers for each user, follower attributes, and user attributes, from a known SNS service. The learning unit 1051 of the server 10 acquires posted articles including image data posted in the past by the user from the SNS service. The learning unit 1051 of the server 10 uses the image data included in the posted article as input data and applies it while changing the learning parameters of the deep neural network included in the generation model 1018, thereby generating a plurality of generated data. At this time, the learning unit 1051 of the server 10 may include the event attributes obtained by applying the image data included in the posted article as input data to the event model 1021 in the input data applied to the generation model 1018. good.
The learning unit 1051 of the server 10 uses the user's SNS attributes of the posted article and the plurality of generated data as input data and applies them to the evaluation model 1017, thereby obtaining a response index for each of the plurality of generated data. The learning unit 1051 of the server 10 optimizes the learning parameters of the deep neural network included in the generative model 1018 so as to obtain a larger reverberation index.
As a result, it is possible to obtain a generation model 1018 that uses image data as input data and outputs generated data that provides a larger reverberation index.

The learning unit 1051 of the server 10 may use any learning model such as a GAN (Generative Adversarial Network) as the generative model 1018.

<Learning process of region extraction model 1019>
The learning process of the region extraction model 1019 is a process of learning the learning parameters of the deep neural network included in the region extraction model 1019 by deep learning.

<Overview of learning process of region extraction model 1019>
The learning process of the region extraction model 1019 is included in the region extraction model 1019 so that image data is used as input data (input vector) and image data from which a region is extracted (partial image data) from which a larger reverberation index is obtained is output. This process uses deep learning to learn the learning parameters of a deep neural network.

<Details of learning process of region extraction model 1019>
The learning unit 1051 of the server 10 acquires SNS attributes of various users in the SNS service, including the number of followers for each user, follower attributes, and user attributes, from a known SNS service. The learning unit 1051 of the server 10 acquires posted articles including image data posted in the past by the user from the SNS service. The learning unit 1051 of the server 10 uses the image data included in the posted article as input data and applies the deep neural network included in the region extraction model 1019 while changing the learning parameters to generate a plurality of partial image data. At this time, the learning unit 1051 of the server 10 includes the event attribute obtained by applying the image data included in the posted article as input data to the event model 1021 in the input data applied to the region extraction model 1019. Also good.
The learning unit 1051 of the server 10 uses the SNS attribute of the user of the posted article and the plurality of partial image data as input data and applies them to the evaluation model 1017, thereby obtaining a response index for each of the plurality of partial image data. The learning unit 1051 of the server 10 optimizes the learning parameters of the deep neural network included in the region extraction model 1019 so as to obtain a larger reverberation index.
As a result, it is possible to obtain a region extraction model 1019 that uses image data as input data and outputs partial image data from which a larger reverberation index can be obtained.

The learning unit 1051 of the server 10 may use the region extraction model 1019 as a learning model used in any object detection method such as R-CNN, Fast R-CNN, Faster R-CNN, YOLO, or SSD. In this case, the learning unit 1051 of the server 10 can identify the category of the object included in the image data and the image range including the object. At this time, the learning unit 1051 of the server 10 detects multiple objects included in the image data, and uses the category of the detected object (person, animal, car, etc.), the image size of the partial image data, etc. as learning parameters. good. As a result, it is possible to obtain a region extraction model 1019 that can obtain partial image data suitable for the object category and image size that can be expected to have a higher reverberation index.

<Learning process of image processing model 1020>
The learning process of the image processing model 1020 is a process of learning the learning parameters of the deep neural network included in the image processing model 1020 by deep learning.

<Outline of learning process of image processing model 1020 (first embodiment)>
The learning process of the image processing model 1020 is included in the image processing model 1020 so that image data is used as input data (input vector) and image processed image data (image processing data) from which a larger reverberation index is obtained is output. This process uses deep learning to learn the learning parameters of a deep neural network. At this time, the learning unit 1051 of the server 10 applies the event attributes obtained by applying the image data included in the posted article to the event model 1021 as input data to the image processing model 1020 (first embodiment). It may also be included in the input data.

<Details of learning processing of image processing model 1020 (first embodiment)>
The learning unit 1051 of the server 10 acquires SNS attributes of various users in the SNS service, including the number of followers for each user, follower attributes, and user attributes, from a known SNS service. The learning unit 1051 of the server 10 acquires posted articles including image data posted in the past by the user from the SNS service. The learning unit 1051 of the server 10 uses the image data included in the posted article as input data, applies it while changing the learning parameters of the deep neural network included in the image processing model 1020 (first embodiment), and performs multiple image processing. Generate data.
The learning unit 1051 of the server 10 applies the SNS attribute of the user of the posted article and the plurality of image processing data as input data to the evaluation model 1017, thereby obtaining a response index for each of the plurality of image processing data. The learning unit 1051 of the server 10 optimizes the learning parameters of the deep neural network included in the image processing model 1020 (first embodiment) so as to obtain a larger reverberation index.
Thereby, it is possible to obtain an image processing model 1020 (first embodiment) that uses image data as input data and outputs image processing data that can obtain a larger reverberation index.

<Overview of learning processing of image processing model 1020 (second embodiment)>
The learning process of the image processing model 1020 (second embodiment) uses image data as input data (input vector) and outputs image processing contents that can obtain a larger reverberation index. This process uses deep learning to learn the learning parameters of the deep neural network included in example). At this time, the learning unit 1051 of the server 10 applies the event attributes obtained by applying the image data included in the posted article to the event model 1021 as input data to the image processing model 1020 (second embodiment). It may also be included in the input data.
Specifically, the image processing model 1020 (second embodiment) in the learning process (second embodiment) of the image processing model 1020 (second embodiment) uses image data as input data (input vector) to This is a learning model that outputs the image processing ID in the image processing table 1016 from which the reverberation index is obtained.
Note that in this disclosure, when simply referred to as the image processing model 1020, the image processing table 1016 is used based on the image processing ID output by the image processing model 1020 (second embodiment) using image data as input data (input vector). The identified image processing content is used as a learning model whose output data is image processing data applied to the image data.

<Details of learning processing of image processing model 1020 (second embodiment)>
The learning unit 1051 of the server 10 generates image data by applying the image processing contents stored in the image processing table 1016 to the image data included in the posted article.
The learning unit 1051 of the server 10 applies the SNS attribute of the user of the posted article and the plurality of image processing data as input data to the evaluation model 1017, thereby obtaining a response index for each of the plurality of image processing data. The learning unit 1051 of the server 10 optimizes the learning parameters of the deep neural network included in the image processing model 1020 (second embodiment) so as to obtain an image processing ID that provides a larger reverberation index.
As a result, it is possible to obtain an image processing model 1020 (second embodiment) that uses image data as input data and outputs an image processing ID that provides a larger reverberation index.

<Learning process of event model 1021>
The learning process of the event model 1021 is a process of learning the learning parameters of the deep neural network included in the event model 1021 by deep learning.

<Overview of learning process of event model 1021>
The learning process of the event model 1021 uses image data as input data (input vector), and performs deep learning on the learning parameters of the deep neural network included in the event model 1021 so as to output generated data from which event attributes of the image data can be obtained. This is a learning process. The event model 1021 may be a classification model in which one or more event attributes are output from a plurality of predetermined event attributes. The event model 1021 may be a generative model that outputs event attributes as image captions.

<Details of learning process of event model 1021>
The learning unit 1051 of the server 10 acquires posted articles including image data posted in the past by various users of the SNS service from the SNS service. The service provider creates training data by adding event attributes (annotation) to a certain number of posted articles. Note that the training data may be created using information such as text and hashtags included in the posted article, or the work of creating the training data may be automated and made more efficient.
The learning unit 1051 of the server 10 creates learning data for each posted article using the image data included in the posted article as input data (input vector) so that the event attribute becomes output data (teacher data). Note that audio data may be included in the input data.
The learning unit 1051 of the server 10 creates datasets such as training data, test data, and verification data for learning the deep neural network of the event model 1021 based on the learning data.
The learning unit 1051 of the server 10 uses deep learning to learn the learning parameters of the deep neural network included in the event model 1021 based on the created data set.

<Learning process of text model 1022>
The learning process of the text model 1022 is a process of learning learning parameters of a deep neural network included in the text model 1022 by deep learning.

<Overview of learning process of text model 1022>
The learning process of the text model 1022 uses image data as input data (input vector), and uses deep learning to set the learning parameters of the deep neural network included in the text model 1022 so as to output generated data from which texts and hashtags can be obtained. This is a learning process.
The learning unit 1051 of the server 10 may include the event attribute obtained by applying the image data included in the posted article to the event model 1021 as input data to the input data applied to the text model 1022.
Text model 1022 may be configured to output either text or hashtags.

<Details of learning process of text model 1022>
The learning unit 1051 of the server 10 acquires posted articles including image data posted in the past by various users of the SNS service from the SNS service.
The learning unit 1051 of the server 10 creates learning data for each posted article using the image data included in the posted article as input data (input vector) so that the text and hashtag of the posted article become output data.
The learning unit 1051 of the server 10 creates datasets such as training data, test data, and verification data for learning the deep neural network of the text model 1022 based on the learning data.
The learning unit 1051 of the server 10 uses deep learning to learn the learning parameters of the deep neural network included in the text model 1022 based on the created data set.

<Modified example>
In the present disclosure, instead of image data shot by the shooting terminal 30, video data or audio data shot by the shooting terminal 30 is edited into video data or audio data of a certain length, respectively, and the SNS Posting processing may be applied to post video data or audio data to SNS. In addition, the SNS posting process may be applied to any digital content instead of image data.

In the event inference process, an event attribute may be inferred based on other digital content instead of image data. In this case, the event model 1021 becomes a learning model learned using the digital content as input data.

In the image generation process (first embodiment), instead of image data, the process may infer generated data based on other digital content. In this case, the image processing model 1020 becomes a learning model trained using the digital content as input data.

In the text/hashtag generation process, the generated data may be inferred based on other digital content instead of image data. In this case, the text model 1022 becomes a learning model trained using the digital content as input data.

In the evaluation process, a reaction index may be inferred based on other digital content instead of image data. In this case, the evaluation unit 1046 becomes a learning model trained using the digital content as input data.

<Basic hardware configuration of the computer>
FIG. 11 is a block diagram showing the basic hardware configuration of the computer 90. The computer 90 includes at least a processor 901, a main storage device 902, an auxiliary storage device 903, and a communication IF 991 (interface). These are electrically connected to each other by a communication bus 921.

The processor 901 is hardware for executing an instruction set written in a program. The processor 901 includes an arithmetic unit, registers, peripheral circuits, and the like.

The main storage device 902 is for temporarily storing programs, data processed by the programs, and the like. For example, it is a volatile memory such as DRAM (Dynamic Random Access Memory).

The auxiliary storage device 903 is a storage device for storing data and programs. Examples include flash memory, HDD (Hard Disc Drive), magneto-optical disk, CD-ROM, DVD-ROM, semiconductor memory, and the like.

The communication IF 991 is an interface for inputting and outputting signals for communicating with other computers via a network using a wired or wireless communication standard.
The network is composed of various mobile communication systems constructed using the Internet, LAN, wireless base stations, and the like. For example, the network includes 3G, 4G, 5G mobile communication systems, LTE (Long Term Evolution), a wireless network (for example, Wi-Fi (registered trademark)) that can be connected to the Internet through a predetermined access point, and the like. When connecting wirelessly, communication protocols include, for example, Z-Wave (registered trademark), ZigBee (registered trademark), Bluetooth (registered trademark), and the like. In the case of a wired connection, the network includes a network that is directly connected using a USB (Universal Serial Bus) cable or the like.

Note that the computer 90 can be virtually realized by distributing all or part of each hardware configuration to a plurality of computers 90 and interconnecting them via a network. In this way, the concept of the computer 90 includes not only the computer 90 housed in a single housing or case, but also a virtualized computer system.

<Basic functional configuration of computer 90>
The functional configuration of the computer realized by the basic hardware configuration of the computer 90 (FIG. 11) will be described. The computer includes at least functional units of a control section, a storage section, and a communication section.

Note that the functional units included in the computer 90 can also be implemented by distributing all or part of each functional unit to a plurality of computers 90 interconnected via a network. The computer 90 is a concept that includes not only a single computer 90 but also a virtualized computer system.

The control unit is realized by the processor 901 reading various programs stored in the auxiliary storage device 903, loading them into the main storage device 902, and executing processing according to the programs. The control unit can implement a functional unit that performs various information processing depending on the type of program. Thereby, the computer is realized as an information processing device that performs information processing.

The storage unit is realized by a main storage device 902 and an auxiliary storage device 903. The storage unit stores data, various programs, and various databases. Further, the processor 901 can secure a storage area corresponding to the storage unit in the main storage device 902 or the auxiliary storage device 903 according to the program. Further, the control unit can cause the processor 901 to execute processing for adding, updating, and deleting data stored in the storage unit according to various programs.

A database refers to a relational database, which is used to manage a data set called a master, which is a tabular table whose structure is defined by rows and columns, in relation to each other. In a database, a table is called a table or a master, a table column is called a column, and a table row is called a record. In a relational database, you can set and associate relationships between tables and masters.
Usually, each table and each master is set with a column that serves as a primary key to uniquely identify a record, but it is not essential to set a primary key on a column. The control unit can cause the processor 901 to add, delete, or update records to a specific table or master stored in the storage unit according to various programs.

Note that the database and master in the present disclosure may include any data structure (list, dictionary, associative array, object, etc.) in which information is structurally defined. Data structures include data that can be considered as a data structure by combining data with functions, classes, methods, etc. written in any programming language.

The communication unit is realized by a communication IF 991. The communication unit realizes a function of communicating with other computers 90 via a network. The communication unit can receive information transmitted from other computers 90 and input it to the control unit. The control unit can cause the processor 901 to execute information processing on the received information according to various programs. Further, the communication unit can transmit information output from the control unit to another computer 90.

<Additional notes>
The matters explained in each of the above embodiments are additionally described below.

(Additional note 1)
A program to be executed by a computer including a processor and a storage unit, the program includes an acquisition step (S101) for acquiring digital content, and generating data based on the digital content acquired in the acquisition step. A generation step (S103) in which the program executes a generation step (S103), wherein the generation step is a step of generating generation data such that a response index becomes larger by using digital content as input data.
Thereby, it is possible to generate generated data that can be expected to have a greater response in SNS services.

(Additional note 2)
The program according to appendix 1, wherein the digital content is image or video data.
Thereby, it is possible to generate generated data that can be expected to have a greater response in SNS services.

(Additional note 3)
The program according to appendix 1 or 2, wherein the generation step is a step of generating generated data by applying a generation model to the digital content acquired in the acquisition step.
Thereby, it is possible to generate generated data that can be expected to have a greater response in SNS services.

(Additional note 4)
The generation step includes a step of generating partial image data by applying a region extraction model to the digital content acquired in the acquisition step, and a step of generating partial image data by applying an image processing model to the generated partial image data. The program according to supplementary note 2, including the step of generating generated data.
Thereby, the amount of calculation can be saved by applying the image processing model to the region extracted by the region extraction model.

(Appendix 5)
The program causes the processor to execute an echo inference step (S105) of inferring a reverberation index of the generated data, and the reverberation index inferred in the echo inference step is used when transmitting the generated data to an external SNS service. The program according to any one of appendices 1 to 4, which is calculated based on the SNS attributes of the user who uses it.
Thereby, it is possible to infer a more accurate response index in accordance with the user's SNS attributes when transmitting to the SNS service.

(Appendix 6)
The program according to appendix 5, wherein the SNS attribute includes at least one of the number of followers of the user, a follower attribute, and a user attribute.
Thereby, it is possible to infer a more accurate response index in accordance with the user's SNS attributes when transmitting to the SNS service. For example, users with a large number of followers are expected to have a higher response index to posted articles. Furthermore, since reactions to posted articles vary depending on follower attributes, a more accurate reaction index can be inferred by taking account of follower attributes.

(Appendix 7)
The program according to any one of Supplementary notes 1 to 6, wherein the program causes the processor to execute a sending step (S107) of sending the generated data to an external SNS service.
Thereby, generated data that can be expected to have a greater response in the SNS service can be transmitted and posted to the SNS service.

(Appendix 8)
The program causes the processor to execute a sending step (S107) of sending the generated data to an external SNS service. The program according to appendix 5 or 6, which is the step of transmitting to an SNS service (S106).
As a result, it is possible to reduce the posting of generated data that cannot be expected to have a positive response to the SNS service. It is possible to send and post to SNS services more efficiently.

(Appendix 9)
The program causes the processor to execute an event inference step (S102) of inferring event attributes related to the digital content based on the digital content, and the generation step is a step of generating generated data based on the event attributes. The program described in any one of 8 to 9.
As a result, it is possible to generate data that is appropriate for the event and that can be expected to have a great response based on the event attributes.

(Appendix 10)
The program instructs the processor to perform an event inference step (S102) of inferring event attributes regarding the digital content based on the digital content, and a text generating at least one of text information or a hashtag based on the digital content and the event attributes. The program according to any one of Supplementary Notes 1 to 9, which executes the generation step.
As a result, it is possible to generate text and hashtags that match the event and can be expected to receive a great response based on the event attributes.

(Appendix 11)
An information processing apparatus including a processor and a storage unit, the processor includes an acquisition step (S101) of acquiring digital content, and a generation step (S103) of generating generated data based on the digital content acquired in the acquisition step. , and the generating step is a step of generating generated data such that a reverberation index becomes larger by using digital content as input data.
Thereby, it is possible to generate generated data that can be expected to have a greater response in SNS services.

(Appendix 12)
An information processing system including a processor, an information processing device including a storage unit, and a photographing terminal, the processor having an acquisition step (S101) of acquiring digital content from the photographing terminal, and a digital content acquired in the acquisition step. A generation step (S103) of generating generated data based on the information processing system, wherein the generating step is a step of generating generated data such that a response index becomes larger by using digital content as input data.
Thereby, it is possible to generate generated data that can be expected to have a greater response in SNS services.

(Appendix 13)
An information processing method executed by a computer including a processor and a storage unit, the method comprising: an acquisition step (S101) of acquiring digital content; and a generation step of generating generated data based on the digital content acquired in the acquisition step. step (S103), and the generation step is a step of generating generated data that has a larger response index using digital content as input data.
Thereby, it is possible to generate generated data that can be expected to have a greater response in SNS services.

Reference Signs List 1 information processing system, 10 server, 101 storage unit, 103 control unit, 20A, 20B, 20C user terminal, 201 storage unit, 204 control unit, 30A, 30B, 30C photographing terminal, 301 storage unit, 304 control unit

Claims (13)

A program to be executed by a computer including a processor and a storage unit,
The program causes the processor to:
an acquisition step of acquiring digital content;
a generation step of generating generated data based on the digital content acquired in the acquisition step;
run the
The generating step is a step of generating generated data that has a larger response index using the digital content as input data.
program. The digital content is image or video data,
The program according to claim 1. The generation step is a step of generating generation data by applying a generation model to the digital content acquired in the acquisition step.
The program according to claim 1 or 2. The generation step includes:
generating partial image data by applying a region extraction model to the digital content acquired in the acquisition step;
generating generated data by applying an image processing model to the generated partial image data;
including,
The program according to claim 2. The program causes the processor to:
a reverberation inference step of inferring a reverberation index of the generated data;
run the
The response index inferred in the response inference step is calculated based on the SNS attribute of the user used when transmitting the generated data to an external SNS service.
The program according to any one of claims 1 to 4. The SNS attribute includes at least one of the number of followers of the user, a follower attribute, and a user attribute.
The program according to claim 5. The program causes the processor to:
a sending step of sending the generated data to an external SNS service;
The program according to any one of claims 1 to 6, which causes the program to execute. The program causes the processor to:
a sending step of sending the generated data to an external SNS service;
run the
The sending step is a step of sending the generated data to an external SNS service when the response index satisfies a predetermined condition.
The program according to claim 5 or 6. The program causes the processor to:
an event inference step of inferring event attributes regarding the digital content based on the digital content;
run the
The generation step is a step of generating generation data based on the event attribute,
The program according to any one of claims 1 to 8. The program causes the processor to:
an event inference step of inferring event attributes regarding the digital content based on the digital content;
a text generation step of generating at least one of text information or a hashtag based on the digital content and the event attribute;
The program according to any one of claims 1 to 9, which causes the program to execute. An information processing device comprising a processor and a storage unit,
the processor;
an acquisition step of acquiring digital content;
a generation step of generating generated data based on the digital content acquired in the acquisition step;
run the
The generating step is a step of generating generated data that has a larger response index using the digital content as input data.
Information processing device. An information processing system comprising a processor, an information processing device including a storage unit, and a photographing terminal,
the processor;
an acquisition step of acquiring digital content from the photographing terminal;
a generation step of generating generated data based on the digital content acquired in the acquisition step;
run the
The generating step is a step of generating generated data that has a larger response index using the digital content as input data.
Information processing system. An information processing method executed by a computer comprising a processor and a storage unit,
the processor;
an acquisition step of acquiring digital content;
a generation step of generating generated data based on the digital content acquired in the acquisition step;
run the
The generating step is a step of generating generated data that has a larger response index using the digital content as input data.
Information processing method.